The potential for measuring ethnicity and health in a multicultural milieu - the case of type 2 diabetes in Australia

ObjectiveEthnicity influences health in many ways. For example, type 2&nbsp;diabetes (T2DM) is disproportionately prevalent among certain ethnic groups.&nbsp;Assessing ethnicity is difficult, and numerous proxy measures are used to&nbsp;capture its various components. Australian guidelines specify a set of&nbsp;variables for measuring ethnicity, and how such parameters should be&nbsp;categorised. Using T2DM data collections as an illustrative example, this&nbsp;study sought to examine how ethnicity is measured in Australian health&nbsp;databases and, by comparing current practice with Australia&rsquo;s existing&nbsp;benchmark recommendations, to identify potential areas for improvement of&nbsp;the health data landscape.DesignWe identified databases containing information from which ethnic&nbsp;group-specific estimates of T2DM burden may be gleaned. For each&nbsp;database, details regarding ethnicity variables were extracted, and compared&nbsp;with the Australian guidelines.&nbsp;ResultsData collection instruments for 32 relevant databases were reviewed.&nbsp;Birthplace was recorded in 27 databases (84%), but mode of birthplace&nbsp;assessment varied. Indigenous status was commonly recorded (78%, n=25), but&nbsp;only nine databases recorded other aspects of self-perceived race/ethnicity. Of&nbsp;28 survey/audit databases, 14 accommodated linguistic preferences other than&nbsp;English, and 11 either excluded non-English speakers or those for whom a&nbsp;translator was not available, or only offered questionnaires in English.ConclusionsConsiderable variation exists in the measurement of ethnicity in&nbsp;Australian health data- sets. While various markers of ethnicity provide&nbsp;complementary information about the ethnic profile within a data-set, nonuniform&nbsp;measurement renders comparison between data-sets difficult. A&nbsp;standardised approach is necessary, and identifying the ethnicity variables&nbsp;that are particularly relevant to the health sector is warranted. Including self identified&nbsp;ethnicity in Australia&rsquo;s set of recommended indicators and as a core&nbsp;component of the national census should be considered. Globalisation and&nbsp;increasing migration mean that these findings have implications internationally,&nbsp;including for multi-ethnic countries throughout North America and&nbsp;Europe.</div

Preventing diabetes through a lifestyle modification program that works

The Greater Green Triangle Diabetes Prevention Project was a national demonstrator program that was conducted in Hamilton, Horsham and Mount Gambier by the GGT UDRH in 2004 to 2006. The project was based on the Finnish Diabetes Prevention Study and the Good Ageing in Lahti Region Lifestyle Implementation Trial. It involved a series of group education sessions delivered to people at high risk of developing diabetes. As the positive effect of diabetes prevention programs is already well established, the aim of this study was to evaluate the feasibility of delivering a structured group-based lifestyle modification program in Australian primary care settings with modest resources. A follow-up investigation looked at whether gains achieved by the intervention were sustained longer term and whether telephone support would provide better outcomes

Evaluation of AUSDRISK as a screening tool for lifestyle modification programs: international implications for policy and cost-effectiveness

OBJECTIVE: To evaluate the current use of Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK) as a screening tool to identify individuals at high risk of developing type 2 diabetes for entry into lifestyle modification programs. RESEARCH DESIGN AND METHODS: AUSDRISK scores were calculated from participants aged 40-74 years in the Greater Green Triangle Risk Factor Study, a cross-sectional population survey in 3 regions of Southwest Victoria, Australia, 2004-2006. Biomedical profiles of AUSDRISK risk categories were determined along with estimates of the Victorian population included at various cut-off scores. Sensitivity, specificity, positive predictive value (PPV), negative predictive value, and receiver operating characteristics were calculated for AUSDRISK in determining fasting plasma glucose (FPG) &ge;6.1 mmol/L. RESULTS: Increasing AUSDRISK scores were associated with an increase in weight, body mass index, FPG, and metabolic syndrome. Increasing the minimum cut-off score also increased the proportion of individuals who were obese and centrally obese, had impaired fasting glucose (IFG) and metabolic syndrome. An AUSDRISK score of &ge;12 was estimated to include 39.5% of the Victorian population aged 40-74 (916 000), while a score of &ge;20 would include only 5.2% of the same population (120 000). At AUSDRISK&ge;20, the PPV for detecting FPG&ge;6.1 mmol/L was 28.4%. CONCLUSIONS: AUSDRISK is powered to predict those with IFG and undiagnosed type 2 diabetes, but its effectiveness as the sole determinant for entry into a lifestyle modification program is questionable given the large proportion of the population screened-in using the current minimum cut-off of &ge;12. AUSDRISK should be used in conjunction with oral glucose tolerance testing, fasting glucose, or glycated hemoglobin to identify those individuals at highest risk of progression to type 2 diabetes, who should be the primary targets for lifestyle modification

Occupational differences, cardiovascular risk factors and lifestyle habits in South Eastern rural Australia

BACKGROUND: In rural and remote Australia, cardiovascular mortality and morbidity rates are higher than metropolitan rates.This study analysed cardiovascular and other chronic disease risk factors and related health behaviours by occupational status, to determine whether agricultural workers have higher cardiovascular disease (CVD) risk than other rural workers. METHODS: Cross-sectional surveys in three rural regions of South Eastern Australia (2004-2006). A stratified random sample of 1001 men and women aged 25-74 from electoral rolls were categorised by occupation into agricultural workers (men = 214, women = 79), technicians (men = 123), managers (men = 148, women = 272) and 'home duties' (women = 165). Data were collected from self-administered questionnaire, physical measurements and laboratory tests. Cardiovascular disease (CVD) and coronary heart disease (CHD) risk were assessed by Framingham 5 years risk calculation. RESULTS: Amongst men, agricultural workers had higher occupational physical activity levels, healthier more traditional diet, lower alcohol consumption, lower fasting plasma glucose, the lowest proportion of daily smokers and lower age-adjusted 5 year CVD and CHD risk scores.Amongst women, managers were younger with higher HDL cholesterol, lower systolic blood pressure, less hypertension, lower waist circumference, less self-reported diabetes and better 5 year CVD and CHD risk scores.Agricultural workers did not have higher cardiovascular disease risk than other occupational groups. CONCLUSIONS: Previous studies have suggested that farmers have higher risks of cardiovascular disease but this is because the risk has been compared with non-rural populations. In this study, the comparison has been made with other rural occupations. Cardiovascular risk reduction programs are justified for all. Programs tailored only for agricultural workers are unwarranted

Mothers after gestational diabetes in Australia Diabetes Prevention Program (MAGDA-DPP) post-natal intervention: study protocol for a randomized controlled trial

Background:Gestational diabetes mellitus (GDM) is defined as glucose intolerance with its onset or first recognition during pregnancy. Post-GDM women have a life-time risk exceeding 70% of developing type 2 diabetes mellitus (T2DM). Lifestyle modifications reduce the incidence of T2DM by up to 58% for high-risk individuals.Methods/Design:The Mothers After Gestational Diabetes in Australia Diabetes Prevention Program (MAGDA-DPP) is a randomized controlled trial aiming to assess the effectiveness of a structured diabetes prevention intervention for post-GDM women. This trial has an intervention group participating in a diabetes prevention program (DPP), and a control group receiving usual care from their general practitioners during the same time period. The 12-month intervention comprises an individual session followed by five group sessions at two-week intervals, and two follow-up telephone calls. A total of 574 women will be recruited, with 287 in each arm. The women will undergo blood tests, anthropometric measurements, and self-reported health status, diet, physical activity, quality of life, depression, risk perception and healthcare service usage, at baseline and 12 months. At completion, primary outcome (changes in diabetes risk) and secondary outcome (changes in psychosocial and quality of life measurements and in cardiovascular disease risk factors) will be assessed in both groups.Discussion:This study aims to show whether MAGDA-DPP leads to a reduction in diabetes risk for post-GDM women. The characteristics that predict intervention completion and improvement in clinical and behavioral measures will be useful for further development of DPPs for this population.</span

Challenges of diabetes prevention in the real world : results and lessons from the Melbourne diabetes prevention study

OBJECTIVE: To assess effectiveness and implementability of the public health programme Life! Taking action on diabetes in Australian people at risk of developing type 2 diabetes. RESEARCH DESIGN AND METHODS: Melbourne Diabetes Prevention Study (MDPS) was a unique study assessing effectiveness of Life! that used a randomized controlled trial design. Intervention participants with AUSDRISK score &ge;15 received 1 individual and 5 structured 90 min group sessions. Controls received usual care. Outcome measures were obtained for all participants at baseline and 12 months and, additionally, for intervention participants at 3 months. Per protocol set (PPS) and intention to treat (ITT) analyses were performed. RESULTS: PPS analyses were considered more informative from our study. In PPS analyses, intervention participants significantly improved in weight (-1.13 kg, p=0.016), waist circumference (-1.35 cm, p=0.044), systolic (-5.2 mm Hg, p=0.028) and diastolic blood pressure (-3.2 mm Hg, p=0.030) compared with controls. Based on observed weight change, estimated risk of developing diabetes reduced by 9.6% in the intervention and increased by 3.3% in control participants. Absolute 5-year cardiovascular disease (CVD) risk reduced significantly for intervention participants by 0.97 percentage points from 9.35% (10.4% relative risk reduction). In control participants, the risk increased by 0.11 percentage points (1.3% relative risk increase). The net effect for the change in CVD risk was -1.08 percentage points of absolute risk (p=0.013). CONCLUSIONS: MDPS effectively reduced the risk of diabetes and CVD, but the intervention effect on weight and waist reduction was modest due to the challenges in recruiting high-risk individuals and the abbreviated intervention

Mothers after Gestational Diabetes in Australia (MAGDA): A Randomised Controlled Trial of a Postnatal Diabetes Prevention Program

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background Gestational diabetes mellitus (GDM) is an increasingly prevalent risk factor for type 2 diabetes. We evaluated the effectiveness of a group-based lifestyle modification program in mothers with prior GDM within their first postnatal year. Methods and Findings In this study, 573 women were randomised to either the intervention (n = 284) or usual care (n = 289). At baseline, 10% had impaired glucose tolerance and 2% impaired fasting glucose. The diabetes prevention intervention comprised one individual session, five group sessions, and two telephone sessions. Primary outcomes were changes in diabetes risk factors (weight, waist circumference, and fasting blood glucose), and secondary outcomes included achievement of lifestyle modification goals and changes in depression score and cardiovascular disease risk factors. The mean changes (intention-to-treat [ITT] analysis) over 12 mo were as follows: −0.23 kg body weight in intervention group (95% CI −0.89, 0.43) compared with +0.72 kg in usual care group (95% CI 0.09, 1.35) (change difference −0.95 kg, 95% CI −1.87, −0.04; group by treatment interaction p = 0.04); −2.24 cm waist measurement in intervention group (95% CI −3.01, −1.42) compared with −1.74 cm in usual care group (95% CI −2.52, −0.96) (change difference −0.50 cm, 95% CI −1.63, 0.63; group by treatment interaction p = 0.389); and +0.18 mmol/l fasting blood glucose in intervention group (95% CI 0.11, 0.24) compared with +0.22 mmol/l in usual care group (95% CI 0.16, 0.29) (change difference −0.05 mmol/l, 95% CI −0.14, 0.05; group by treatment interaction p = 0.331). Only 10% of women attended all sessions, 53% attended one individual and at least one group session, and 34% attended no sessions. Loss to follow-up was 27% and 21% for the intervention and control groups, respectively, primarily due to subsequent pregnancies. Study limitations include low exposure to the full intervention and glucose metabolism profiles being near normal at baseline. Conclusions Although a 1-kg weight difference has the potential to be significant for reducing diabetes risk, the level of engagement during the first postnatal year was low. Further research is needed to improve engagement, including participant involvement in study design; it is potentially more effective to implement annual diabetes screening until women develop prediabetes before offering an intervention. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN1261000033806

Recruitment into diabetes prevention programs : what is the impact of errors in self-reported measures of obesity?

BackgroundError in self-reported measures of obesity has been frequently described, but the effect of self-reported error on recruitment into diabetes prevention programs is not well established. The aim of this study was to examine the effect of using self-reported obesity data from the Finnish diabetes risk score (FINDRISC) on recruitment into the Greater Green Triangle Diabetes Prevention Project (GGT DPP).MethodsThe GGT DPP was a structured group-based lifestyle modification program delivered in primary health care settings in South-Eastern Australia. Between 2004&ndash;05, 850 FINDRISC forms were collected during recruitment for the GGT DPP. Eligible individuals, at moderate to high risk of developing diabetes, were invited to undertake baseline tests, including anthropometric measurements performed by specially trained nurses. In addition to errors in calculating total risk scores, accuracy of self-reported data (height, weight, waist circumference (WC) and Body Mass Index (BMI)) from FINDRISCs was compared with baseline data, with impact on participation eligibility presented.ResultsOverall, calculation errors impacted on eligibility in 18 cases (2.1%). Of n&thinsp;=&thinsp;279 GGT DPP participants with measured data, errors (total score calculation, BMI or WC) in self-report were found in n&thinsp;=&thinsp;90 (32.3%). These errors were equally likely to result in under- or over-reported risk. Under-reporting was more common in those reporting lower risk scores (Spearman-rho&thinsp;=&thinsp;&minus;0.226, p-value&thinsp;&lt;&thinsp;0.001). However, underestimation resulted in only 6% of individuals at high risk of diabetes being incorrectly categorised as moderate or low risk of diabetes.ConclusionsOverall FINDRISC was found to be an effective tool to screen and recruit participants at moderate to high risk of diabetes, accurately categorising levels of overweight and obesity using self-report data. The results could be generalisable to other diabetes prevention programs using screening tools which include self-reported levels of obesity.<br /

Implementation salvage experiences from the Melbourne diabetes prevention study

Background Many public health interventions based on apparently sound evidence from randomised controlled trials encounter difficulties when being scaled up within health systems. Even under the best of circumstances, implementation is exceedingly difficult. In this paper we will describe the implementation salvage experiences from the Melbourne Diabetes Prevention Study, which is a randomised controlled trial of the effectiveness and cost-effectiveness nested in the state-wide Life! Taking Action on Diabetes program in Victoria, Australia.Discussion The Melbourne Diabetes Prevention Study sits within an evolving larger scale implementation project, the Life! program. Changes that occurred during the roll-out of that program had a direct impact on the process of conducting this trial. The issues and methods of recovery the study team encountered were conceptualised using an implementation salvage strategies framework. The specific issues the study team came across included continuity of the state funding for Life! program and structural changes to the Life! program which consisted of adjustments to eligibility criteria, referral processes, structure and content, as well as alternative program delivery for different population groups. Staff turnover, recruitment problems, setting and venue concerns, availability of potential participants and participant characteristics were also identified as evaluation roadblocks. Each issue and corresponding salvage strategy is presented.Summary The experiences of conducting such a novel trial as the preliminary Melbourne Diabetes Prevention Study have been invaluable. The lessons learnt and knowledge gained will inform the future execution of this trial in the coming years. We anticipate that these results will also be beneficial to other researchers conducting similar trials in the public health field. We recommend that researchers openly share their experiences, barriers and challenges when conducting randomised controlled trials and implementation research. We encourage them to describe the factors that may have inhibited or enhanced the desired outcomes so that the academic community can learn and expand the research foundation of implementation salvage.<br /